-k = 2
-
-n = nrow(Y)
-m = ncol(Y)
-p = ncol(X)
-
-Zinit1 = array(0, dim=c(n))
-betaInit1 = array(0, dim=c(p,m,k))
-sigmaInit1 = array(0, dim = c(m,m,k))
-phiInit1 = array(0, dim = c(p,m,k))
-rhoInit1 = array(0, dim = c(m,m,k))
-Gam = matrix(0, n, k)
-piInit1 = matrix(0,k)
-gamInit1 = array(0, dim=c(n,k))
-LLFinit1 = list()
-
-require(MASS) #Moore-Penrose generalized inverse of matrix
-
- distance_clus = dist(X)
- tree_hier = hclust(distance_clus)
- Zinit1 = cutree(tree_hier, k)
- sum(Zinit1==1)
-
- for(r in 1:k)
- {
- Z = Zinit1
- Z_indice = seq_len(n)[Z == r] #renvoit les indices où Z==r
- if (length(Z_indice) == 1) {
- betaInit1[,,r] = ginv(crossprod(t(X[Z_indice,]))) %*%
- crossprod(t(X[Z_indice,]), Y[Z_indice,])
- } else {
- betaInit1[,,r] = ginv(crossprod(X[Z_indice,])) %*%
- crossprod(X[Z_indice,], Y[Z_indice,])
- }
- sigmaInit1[,,r] = diag(m)
- phiInit1[,,r] = betaInit1[,,r] #/ sigmaInit1[,,r]
- rhoInit1[,,r] = solve(sigmaInit1[,,r])
- piInit1[r] = mean(Z == r)
- }
-
- for(i in 1:n)
- {
- for(r in 1:k)
- {
- dotProduct = tcrossprod(Y[i,]%*%rhoInit1[,,r]-X[i,]%*%phiInit1[,,r])
- Gam[i,r] = piInit1[r]*det(rhoInit1[,,r])*exp(-0.5*dotProduct)
- }
- sumGamI = sum(Gam[i,])
- gamInit1[i,]= Gam[i,] / sumGamI
- }
-
- miniInit = 10
- maxiInit = 101
-
- new_EMG = EMGLLF(phiInit1,rhoInit1,piInit1,gamInit1,miniInit,maxiInit,1,0,X,Y,1e-6)
-
- new_EMG$phi
- new_EMG$pi
- LLFEessai = new_EMG$LLF
- LLFinit1 = LLFEessai[length(LLFEessai)]
-
-
-b = which.max(LLFinit1)
-phiInit = phiInit1[,,,b]
-rhoInit = rhoInit1[,,,b]
-piInit = piInit1[b,]
-gamInit = gamInit1[,,b]